
import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowercase_ 				=			{
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

lowercase_ 				=			{
    """ctrl""": 256,
}

lowercase_ 				=			{
    """Pregnancy""": 168_629,
    """Christianity""": 7_675,
    """Explain""": 106_423,
    """Fitness""": 63_440,
    """Saving""": 63_163,
    """Ask""": 27_171,
    """Ass""": 95_985,
    """Joke""": 163_509,
    """Questions""": 45_622,
    """Thoughts""": 49_605,
    """Retail""": 52_342,
    """Feminism""": 164_338,
    """Writing""": 11_992,
    """Atheism""": 192_263,
    """Netflix""": 48_616,
    """Computing""": 39_639,
    """Opinion""": 43_213,
    """Alone""": 44_967,
    """Funny""": 58_917,
    """Gaming""": 40_358,
    """Human""": 4_088,
    """India""": 1_331,
    """Joker""": 77_138,
    """Diet""": 36_206,
    """Legal""": 11_859,
    """Norman""": 4_939,
    """Tip""": 72_689,
    """Weight""": 52_343,
    """Movies""": 46_273,
    """Running""": 23_425,
    """Science""": 2_090,
    """Horror""": 37_793,
    """Confession""": 60_572,
    """Finance""": 12_250,
    """Politics""": 16_360,
    """Scary""": 191_985,
    """Support""": 12_654,
    """Technologies""": 32_516,
    """Teenage""": 66_160,
    """Event""": 32_769,
    """Learned""": 67_460,
    """Notion""": 182_770,
    """Wikipedia""": 37_583,
    """Books""": 6_665,
    """Extract""": 76_050,
    """Confessions""": 102_701,
    """Conspiracy""": 75_932,
    """Links""": 63_674,
    """Narcissus""": 150_425,
    """Relationship""": 54_766,
    """Relationships""": 134_796,
    """Reviews""": 41_671,
    """News""": 4_256,
    """Translation""": 26_820,
    """multilingual""": 128_406,
}


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		set()
  __SCREAMING_SNAKE_CASE      :	Any					=		word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)							)
    __SCREAMING_SNAKE_CASE      :	Dict					=		char

  __SCREAMING_SNAKE_CASE      :	int					=		set(snake_case							)
  return pairs





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			VOCAB_FILES_NAMES
     lowerCAmelCase_							   =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_							   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_							   =			CONTROL_CODES



     def __init__(				self							:							Dict     ,						_A							:							Union[str, Any]     ,						_A							:							List[Any]     ,						_A							:							Union[str, Any]="<unk>"     ,						**_A							:							Any ):







       """simple docstring"""




       super().__init__(unk_token=_A     ,						**_A )

       with open(_A     ,						encoding='''utf-8''' ) as vocab_handle:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		json.load(_A )
       __SCREAMING_SNAKE_CASE      :	str					=		{v: k for k, v in self.encoder.items()}
       with open(_A     ,						encoding='''utf-8''' ) as merges_handle:
         __SCREAMING_SNAKE_CASE      :	Tuple					=		merges_handle.read().split('''\n''' )[1:-1]
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[tuple(merge.split() ) for merge in merges]
       __SCREAMING_SNAKE_CASE      :	str					=		dict(zip(_A     ,						range(len(_A ) ) ) )
       __SCREAMING_SNAKE_CASE      :	int					=		{}



     @property
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       return len(self.encoder )



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       return dict(self.encoder     ,						**self.added_tokens_encoder )



     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							List[str] ):







       """simple docstring"""




       if token in self.cache:
         return self.cache[token]
       __SCREAMING_SNAKE_CASE      :	List[str]					=		tuple(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		get_pairs(_A )

       if not pairs:
         return token

       while True:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		min(_A     ,						key=lambda _A : self.bpe_ranks.get(_A     ,						float('''inf''' ) ) )
         if bigram not in self.bpe_ranks:
           break
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		bigram
         __SCREAMING_SNAKE_CASE      :	Any					=		[]
         __SCREAMING_SNAKE_CASE      :	str					=		0
         while i < len(_A ):
           try:
             __SCREAMING_SNAKE_CASE      :	Optional[int]					=		word.index(_A     ,						_A )
           except ValueError:
             new_word.extend(word[i:] )
             break
           else:
             new_word.extend(word[i:j] )
             __SCREAMING_SNAKE_CASE      :	Tuple					=		j

           if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
             new_word.append(first + second )
             i += 2
           else:
             new_word.append(word[i] )
             i += 1
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		tuple(_A )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		new_word
         if len(_A ) == 1:
           break
         else:
           __SCREAMING_SNAKE_CASE      :	Dict					=		get_pairs(_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		'''@@ '''.join(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		word[:-4]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		word
       return word



     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							Union[str, Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[]

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		re.findall(r'''\S+\n?'''     ,						_A )

       for token in words:
         split_tokens.extend(list(self.bpe(_A ).split(''' ''' ) ) )
       return split_tokens



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							Optional[Any] ):







       """simple docstring"""




       return self.encoder.get(_A     ,						self.encoder.get(self.unk_token ) )



     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						_A							:							Union[str, Any] ):







       """simple docstring"""




       return self.decoder.get(_A     ,						self.unk_token )



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		''' '''.join(_A ).replace('''@@ '''     ,						'''''' ).strip()
       return out_string




     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							str     ,						_A							:							Optional[str] = None ):







       """simple docstring"""




       if not os.path.isdir(_A ):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
         return
       __SCREAMING_SNAKE_CASE      :	Tuple					=		os.path.join(
           _A     ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		os.path.join(
           _A     ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

       with open(_A     ,						'''w'''     ,						encoding='''utf-8''' ) as f:
         f.write(json.dumps(self.encoder     ,						indent=2     ,						sort_keys=_A     ,						ensure_ascii=_A ) + '''\n''' )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		0
       with open(_A     ,						'''w'''     ,						encoding='''utf-8''' ) as writer:
         writer.write('''#version: 0.2\n''' )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,						key=lambda _A : kv[1] ):
           if index != token_index:
             logger.warning(
                 F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                 ''' Please check that the tokenizer is not corrupted!''' )
             __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		token_index
           writer.write(''' '''.join(_A ) + '''\n''' )
           index += 1

       return vocab_file, merge_file

     # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
     #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
     #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
     #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
     #     return ''.join(tokens_generated_so_far)





'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




